System and method for computing and transmitting parameters in a distributed voice recognition system

Abstract

A system and method for extracting acoustic features and speech activity on a device and transmitting them in a distributed voice recognition system. The distributed voice recognition system includes a local VR engine in a subscriber unit and a server VR engine on a server. The local VR engine comprises a feature extraction (FE) module that extracts features from a speech signal, and a voice activity detection module (VAD) that detects voice activity within a speech signal. The system includes filters, framing and windowing modules, power spectrum analyzers, a neural network, a nonlinear element, and other components to selectively provide an advanced front end vector including predetermined portions of the voice activity detection indication and extracted features from the subscriber unit to the server. The system also includes a module to generate additional feature vectors on the server from the received features using a feed-forward multilayer perceptron (MLP) and providing the same to the speech server.

1 . A digital voice recognition (DVR) system comprising:
 a subscriber unit that receives a speech signal, wherein the subscriber unit includes:
 a voice activity detection (VAD) module that detects voice activity within the speech signal and generates an indication of the detected voice activity, wherein the VAD module comprises:
 a sample rate conversion module, wherein the sample rate conversion module receives processed frames of the speech signal and changes a sample rate of the processed frames of the speech signal to generate sample rate converted frames; 
 a transform module coupled to the sample rate conversion module that calculates cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames; 
 a probability estimator module coupled to the transform module that estimates a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and 
 a threshold module that applies a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is the indication of voice activity; 
 
 a feature extraction (FE) module that extracts at least one speech feature from the speech signal; and 
 a transmitter that transmits the indication of detected voice activity and the at least one speech feature; and 
   a central communications center that receives the indication of detected voice activity and the at least one speech feature from the subscriber unit.   
     
     
         2 . The system of  claim 1 , wherein the central communications center determines at least one linguistic estimate. 
     
     
         3 . The system of  claim 1 , wherein the FE module concatenates the indication of the detected voice activity and the at least one feature. 
     
     
         4 . The system of  claim 1 , wherein the transmitter transmits the indication of the detected voice activity when available. 
     
     
         5 . The system of  claim 1 , wherein the subscriber unit further comprises:
 a framing module that generates a plurality of frames of the speech signal;   a windowing module coupled to the framing module that windows each one of the plurality of frames;   a transform module coupled to the windowing module that computes a magnitude spectrum for each windowed frame;   a power spectrum module coupled to the transform module that computes a power spectrum of the magnitude spectrum;   a filtering module coupled to the power spectrum module that filters the power spectrum; and   a non-linear transformation module coupled to the filtering module that generates non-linear transformation of the filtered power spectrum, wherein the generated non-linear transformation is the processed frames of the speech signal,   wherein the VAD module and the FE module are coupled to the non-linear transformation module.   
     
     
         6 . The system of  claim 5 , wherein the transform module computes the magnitude spectrum via a fast-Fourier transform. 
     
     
         7 . The system of  claim 5 , wherein the filtering module filters the power spectrum by using a complete frequency range of a MEL-warped spectrum. 
     
     
         8 . The system of  claim 1 , wherein the sample rate conversion module downsamples the processed frames of the speech signal by a factor of two. 
     
     
         9 . The system of  claim 1 , wherein the VAD module further comprises a median filter coupled to the threshold module that smoothes the binary feature. 
     
     
         10 . The system of  claim 9 , wherein the median filter is an  11  point median filter. 
     
     
         11 . The system of  claim 9 , wherein the median filter removes short pauses or bursts of speech. 
     
     
         12 . The system of  claim 9 , wherein the median filter adds seven frames before and after the current frame that is estimated to include speech. 
     
     
         13 . The system of  claim 9 , wherein the median filter sets a bit according to whether the current frame is estimated to include speech. 
     
     
         14 . The system of  claim 1 , wherein the probability estimator module receives the current frame, two frames adjacent to the current frame, and two cepstral coefficients calculated by the transform module. 
     
     
         15 . A subscriber unit comprising:
 a microphone that receives speech signals;   a voice activity detection (VAD) module that detects voice activity within the speech signal and generates an indication of the detected voice activity, wherein the VAD module comprises:
 a sample rate conversion module, wherein the sample rate conversion module receives processed frames of the speech signal and changes a sample rate of the processed frames of the speech signal to generate sample rate converted frames; 
 a transform module coupled to the sample rate conversion module that calculates cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames; 
 a probability estimator module coupled to the transform module that estimates a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and 
 a threshold module that applies a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is the indication of voice activity; 
   a feature extraction (FE) module that extracts at least one speech feature from the speech signal; and   a transmitter that transmits the indication of detected voice activity and the at least one speech feature.   
     
     
         16 . The subscriber unit of  claim 15 , wherein the FE module concatenates the indication of the detected voice activity and the at least one feature. 
     
     
         17 . The subscriber unit of  claim 15 , wherein the transmitter transmits the indication of the detected voice activity when available. 
     
     
         18 . The subscriber unit of  claim 15 , further comprising:
 a framing module that generates a plurality of frames of the speech signal;   a windowing module coupled to the framing module that windows each one of the plurality of frames;   a transform module coupled to the windowing module that computes a magnitude spectrum for each windowed frame;   a power spectrum module coupled to the transform module that computes a power spectrum of the magnitude spectrum;   a filtering module coupled to the power spectrum module that filters the power spectrum; and   a non-linear transformation module coupled to the filtering module that generates non-linear transformation of the filtered power spectrum, wherein the generated non-linear transformation is the processed frames of the speech signal,   wherein the VAD module and the FE module are coupled to the non-linear transformation module.   
     
     
         19 . The subscriber unit of  claim 18 , wherein the transform module computes the magnitude spectrum via a fast-Fourier transform. 
     
     
         20 . The subscriber unit of  claim 18 , wherein the filtering module filters the power spectrum by using a complete frequency range of a MEL-warped spectrum. 
     
     
         21 . The subscriber unit of  claim 15 , wherein the sample rate conversion module downsamples the processed frames of the speech signal by a factor of two. 
     
     
         22 . The subscriber unit of  claim 15 , wherein the VAD module further comprises a median filter coupled to the threshold module that smoothes the binary feature. 
     
     
         23 . The subscriber unit of  claim 22 , wherein the median filter is an  11  point median filter. 
     
     
         24 . The subscriber unit of  claim 22 , wherein the median filter removes short pauses or bursts of speech. 
     
     
         25 . The subscriber unit of  claim 22 , wherein the median filter adds seven frames before and after the current frame that is estimated to include speech. 
     
     
         26 . The subscriber unit of  claim 22 , wherein the median filter sets a bit according to whether the current frame is estimated to include speech. 
     
     
         27 . The subscriber unit of  claim 15 , wherein the probability estimator module receives the current frame, two frames adjacent to the current frame, and two cepstral coefficients calculated by the transform module. 
     
     
         28 . A voice activity detection (VAD) processor comprising:
 a sample rate conversion module implemented as hardware in the VAD processor, wherein the sample rate conversion module receives processed frames of a speech signal and changes a sample rate of the processed frames of the speech signal to generate sample rate converted frames;   a transform module implemented as hardware in the VAD processor and coupled to the sample rate conversion module that calculates cepstral coefficients of the sample rate converted frames of the speech signal by performing a discrete cosine transformation of the sample rate converted frames;   a probability estimator module implemented as hardware in the VAD processor and coupled to the transform module that estimates a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and   a threshold module implemented as hardware in the VAD processor that applies a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is an indication of voice activity.   
     
     
         29 . The VAD processor of  claim 28 , wherein the processed frames of the speech signal are generated via:
 a framing module that generates a plurality of frames of the speech signal;   a windowing module coupled to the framing module that windows each one of the plurality of frames;   a transform module coupled to the windowing module that computes a magnitude spectrum for each windowed frame;   a power spectrum module coupled to the transform module that computes a power spectrum of the magnitude spectrum;   a filtering module coupled to the power spectrum module that filters the power spectrum; and   a non-linear transformation module coupled to the filtering module that generates a non-linear transformation of the filtered power spectrum, wherein the non-linear transformation comprises the processed frames of the speech signal.   
     
     
         30 . The VAD processor of  claim 28 , wherein the sample rate conversion module downsamples the processed frames of the speech signal by a factor of two. 
     
     
         31 . The VAD processor of  claim 28  further comprising a median filter coupled to the threshold module that smoothes the binary feature. 
     
     
         32 . The VAD processor of  claim 31 , wherein the median filter is an  11  point median filter. 
     
     
         33 . The VAD processor of  claim 31 , wherein the median filter removes short pauses or bursts of speech. 
     
     
         34 . The VAD processor of  claim 31 , wherein the median filter adds seven frames before and after the current frame that is estimated to include speech. 
     
     
         35 . The VAD processor of  claim 31 , wherein the median filter sets a bit according to whether the current frame is estimated to include speech. 
     
     
         36 . The VAD processor of  claim 31 , wherein the probability estimator module receives the current frame, two frames adjacent to the current frame, and two cepstral coefficients calculated by the transform module. 
     
     
         37 . A method for transmitting speech activity, comprising:
 receiving, via a subscriber unit, a speech signal;   processing, via the subscriber unit, the speech signal;   detecting, via the subscriber unit, voice activity within the speech signal and generating an indication of the detected voice activity, wherein detecting voice activity within the speech signal and generating the indication of the detected voice activity comprises:
 converting a sample rate of the processed speech signal to generate sample rate converted frames; 
 calculating cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames; 
 estimating a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and 
 applying a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is the indication of voice activity; 
   extracting, via the subscriber unit, at least one feature from the speech signal; and   transmitting, via the subscriber unit, the indication of the detected voice activity and the at least one feature.   
     
     
         38 . The method of  claim 37 , wherein transmitting the indication of the detected voice activity and the at least one feature comprises concatenating the indication of the detected voice activity and the at least one feature and transmitting the concatenated indication of the detected voice activity and the at least one feature. 
     
     
         39 . The method of  claim 37 , wherein transmitting the indication of the detected voice activity and the at least one feature comprises transmitting the detected voice activity when available. 
     
     
         40 . The method of  claim 37 , wherein processing the speech signal comprises:
 generating a plurality of frames of the speech signal;   windowing each one of the plurality of frames;   computing a magnitude spectrum for each windowed frame;   computing a power spectrum for each magnitude spectrum;   filtering the power spectrum; and   generating a non-linear transformation of the filtered power spectrum.   
     
     
         41 . The method of  claim 40 , wherein computing the magnitude spectrum comprises performing a fast-Fourier transform. 
     
     
         42 . The method of  claim 40 , wherein filtering the power spectrum comprises filtering based on a complete frequency range of a MEL-warped spectrum. 
     
     
         43 . The method of  claim 37 , wherein converting the sample rate of the processed speech signal comprises downsampling the processed speech signal by a factor of 2. 
     
     
         44 . The method of  claim 37 , further comprising:
 smoothing the binary feature to generate the indication of voice activity.   
     
     
         45 . The method of  claim 37 , further comprising:
 setting a bit according to whether the current frame is estimated to include speech, wherein the bit is the indication of voice activity.   
     
     
         46 . A method for detecting voice activity, comprising:
 processing, via a voice activity detection (VAD) processor, a speech signal to generate processed frames;   converting, via the VAD processor, a sample rate of the processed frames of the speech signal to generate sample rate converted frames;   calculating, via the VAD processor, cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames;   estimating, via the VAD processor, a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and   applying, via the VAD processor, a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is an indication of voice activity.   
     
     
         47 . The method of  claim 46 , wherein processing the speech signal to generate processed frames comprises:
 generating a plurality of frames of the speech signal;   windowing each one of the plurality of frames;   computing a magnitude spectrum for each windowed frame;   computing a power spectrum for each magnitude spectrum;   filtering the power spectrum; and   generating a non-linear transformation of the filtered power spectrum.   
     
     
         48 . The method of  claim 46 , wherein converting a sample rate of the processed speech signal comprises downsampling the processed speech signal by a factor of 2. 
     
     
         49 . The method of  claim 46 , further comprising:
 smoothing the binary feature to generate the indication of voice activity.   
     
     
         50 . The method of  claim 46 , further comprising:
 setting a bit according to whether the current frame is estimated to include speech, wherein the bit is the indication of voice activity.   
     
     
         51 . A computer-readable storage medium comprising one or more memories that store instructions that cause one or more processors to:
 receive a speech signal;   process the speech signal;   detect voice activity within the speech signal and generate an indication of the   detected voice activity, wherein instruction that cause the one or more processors to detect voice activity within the speech signal and generate the indication of the detected voice activity comprises instructions that cause the one or more processors to:
 convert a sample rate of the processed speech signal to generate sample rate converted frames; 
 calculate cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames; 
 estimate a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and 
 apply a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is the indication of voice activity; 
 extract at least one feature from the speech signal; and 
 transmit the indication of the detected voice activity and the at least one feature. 
   
     
     
         52 . The computer-readable storage medium of  claim 51 , wherein instructions that cause one or more processors to transmit the indication of the detected voice activity and the at least one feature comprises instructions that cause one or more processors to concatenate the indication of the detected voice activity and the at least one feature and transmit the concatenated indication of the detected voice activity and the at least one feature. 
     
     
         53 . The computer-readable storage medium of  claim 51 , wherein instructions that cause one or more processors to transmit the indication of the detected voice activity and the at least one feature comprises instructions that cause one or more processors to transmit the detected voice activity when available. 
     
     
         54 . The computer-readable storage medium of  claim 51 , wherein the instructions that cause the one or more processors to process the speech signal comprises:
 instructions that cause the one or more processors to generate a plurality of frames of the speech signal;   instructions that cause the one or more processors to window each one of the plurality of frames;   instructions that cause the one or more processors to compute a magnitude spectrum for each windowed frame;   instructions that cause the one or more processors to compute a power spectrum for each magnitude spectrum;   instructions that cause the one or more processors to filter the power spectrum; and   instructions that cause the one or more processors to generate a non-linear transformation of the filtered power spectrum.   
     
     
         55 . The computer-readable storage medium of  claim 54 , wherein the instructions that cause the one or more processors to compute the magnitude spectrum comprise instructions that cause the one or more processors to perform a fast-Fourier transform. 
     
     
         56 . The computer-readable storage medium of  claim 54 , wherein the instructions that cause the one or more processors to filter the power spectrum comprise instructions that cause the one or more processors to filter based on a complete frequency range of a MEL-warped spectrum. 
     
     
         57 . The computer-readable storage medium of  claim 51 , wherein the instructions that cause the one or more processors to convert the sample rate the processed speech signal comprise instructions that cause the one or more processors to downsample the processed speech signal by a factor of 2. 
     
     
         58 . The computer-readable storage medium of  claim 51 , further comprising:
 instructions that cause the one or more processors to smooth the binary feature to generate the indication of voice activity.   
     
     
         59 . The computer-readable storage medium of  claim 51 , further comprising:
 instructions that cause the one or more processors to set a bit according to whether a current frame is determined to include speech, wherein the bit is the indication of voice activity.   
     
     
         60 . A computer-readable storage medium comprising one or more memories that store instructions that cause one or more processors to:
 process a speech signal to generate processed frames;   convert a sample rate of processed frames of the speech signal to generate sample rate converted frames;   calculate cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames;   estimate a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and   apply a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is an indication of voice activity.   
     
     
         61 . The computer-readable storage medium of  claim 60 , wherein the instructions that cause one or more processors to process the speech signal to generate processed frames comprise:
 instructions that cause the one or more processors to generate a plurality of frames of the speech signal;   instructions that cause the one or more processors to window each one of the plurality of frames;   instructions that cause the one or more processors to compute a magnitude spectrum for each windowed frame;   instructions that cause the one or more processors to compute a power spectrum for each magnitude spectrum;   instructions that cause the one or more processors to filter the power spectrum; and   instructions that cause the one or more processors to generate a non-linear transformation of the filtered power spectrum.   
     
     
         62 . The computer-readable storage medium of  claim 60 , wherein the instructions that cause the one or more processors to convert the sample rate of processed frames comprise instructions that cause the one or more processors to downsample the processed frames by a factor of 2. 
     
     
         63 . The computer-readable storage medium of  60 , further comprising:
 instructions that cause the one or more processors to smooth the binary feature to generate the indication of voice activity.   
     
     
         64 . The computer-readable storage medium of  claim 60 , further comprising:
 instructions that cause the one or more processors to set a bit according to whether the current frame is estimated to include speech, wherein the bit is the indication of voice activity.   
     
     
         65 . A digital voice recognition (DVR) system comprising:
 means for receiving a speech signal, wherein the means for receiving includes:
 means for detecting voice activity within the speech signal and generating an indication of the detected voice activity, wherein the means for detecting voice activity within the speech signal and generating the indication of the detected voice activity comprises:
 means for processing the speech signal; 
 means for converting a sample rate of the processed speech signal to generate sample rate converted frames; 
 means for calculating cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames; 
 means for estimating a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and 
 means for applying a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is the indication of voice activity; 
 
 means for extracting at least one speech feature from the speech signal; and 
 means for transmitting the indication of detected voice activity and the at least one speech feature; and 
   means for receiving the indication of detected voice activity and the at least one speech feature from the subscriber unit.   
     
     
         66 . The DVR system of  claim 65 , further comprising means for determining at least one linguistic estimate of speech from the at least one speech feature. 
     
     
         67 . The DVR system of  claim 65 , wherein the means for extracting concatenates the indication of the detected voice activity and the at least one feature. 
     
     
         68 . The DVR system of  claim 65 , wherein the means for transmitting transmits the indication of the detected voice activity when available. 
     
     
         69 . The DVR system of  claim 65 , wherein the means for processing comprises:
 means for generating a plurality of frames of the speech signal;   means for windowing each one of the plurality of frames;   means for computing a magnitude spectrum for each windowed frame;   means for computing a power spectrum of the magnitude spectrum;   means for filtering the power spectrum; and   means for generating non-linear transformation of the filtered power spectrum.   
     
     
         70 . The DVR system of  claim 69 , wherein the means for computing the magnitude spectrum comprises means for performing a fast-Fourier transform. 
     
     
         71 . The DVR system of  claim 70 , wherein the means for filtering the power spectrum comprises means for filtering by using a complete frequency range of a MEL warped-spectrum. 
     
     
         72 . The DVR system of  claim 65 , wherein the means for converting the sample rate downsamples the processed speech signal by a factor of two. 
     
     
         73 . The DVR system of  claim 65 , wherein means for detecting voice activity within the speech signal and generating an indication of the detected voice activity further comprises:
 means for smoothing the binary feature.   
     
     
         74 . The DVR system of  claim 73 , wherein the means for smoothing adds seven frames before and after the current frame that is estimated to include speech. 
     
     
         75 . The DVR system of  claim 73 , wherein the means for smoothing comprises means for setting a bit according to whether the current frame is estimated to include speech. 
     
     
         76 . The DVR system of  claim 65 , wherein the means for estimating receives the current frame, two adjacent frames, and two cepstral coefficients calculated by the means for calculating. 
     
     
         77 . A subscriber unit comprising:
 means for receiving speech signals;   means for detecting voice activity within the speech signal and generating an indication of the detected voice activity, wherein the means for detecting voice activity within the speech signal and generating the indication of the detected voice activity comprises:
 means for processing the speech signal; 
 means for converting a sample rate of the processed speech signal to generate sample rate converted frames; 
 means for calculating cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames; 
 means for estimating a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients; and 
   means for applying a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is the indication of voice activity;   means for extracting at least one speech feature from the speech signal; and   means for transmitting the indication of detected voice activity and the at least one speech feature.   
     
     
         78 . The subscriber unit of  claim 77 , wherein the means for extracting concatenates the indication of the detected voice activity and the at least one feature. 
     
     
         79 . The subscriber unit of  claim 77 , wherein the means for transmitting transmits the indication of the detected voice activity when available. 
     
     
         80 . The subscriber unit of  claim 77 ,wherein the means for processing comprises:
 means for generating a plurality of frames of the speech signal;   means for windowing each one of the plurality of frames;   means for computing a magnitude spectrum for each windowed frame;   means for computing a power spectrum of the magnitude spectrum;   means for filtering the power spectrum; and   means for generating non-linear transformation of the filtered power spectrum.   
     
     
         81 . The subscriber unit of  claim 80 , wherein the means for computing the magnitude spectrum comprises means for performing a fast-Fourier transform. 
     
     
         82 . The subscriber unit of  claim 80 , wherein the means for filtering the power spectrum comprises means for filtering by using a complete frequency range of a MEL warped-spectrum. 
     
     
         83 . The subscriber unit of  claim 77 , wherein the means for converting the sample rate downsamples the processed speech signal by a factor of two. 
     
     
         84 . The subscriber unit of  claim 77 , wherein means for detecting voice activity within the speech signal and generating an indication of the detected voice activity further comprises:
 means for smoothing the binary feature.   
     
     
         85 . The subscriber unit of  claim 84 , wherein the means for smoothing adds seven frames before and after the current frame that is estimated to include speech. 
     
     
         86 . The subscriber unit of  claim 84 , wherein the means for smoothing comprises means for setting a bit according to whether the current frame is determined to include speech. 
     
     
         87 . The subscriber unit of  claim 77 , wherein the means for estimating receives the current frame, two adjacent frames, and two cepstral coefficients calculated by the means for calculating. 
     
     
         88 . A voice activity detection (VAD) processor comprising:
 means for converting a sample rate of received processed frames of a speech signal to generate sample rate converted frames, wherein the means for converting is implemented as hardware within the VAD processor;   means for calculating cepstral coefficients of the sample rate converted frames by performing a discrete cosine transformation of the sample rate converted frames, wherein the means for calculating is implemented as hardware within the VAD processor;   means for estimating a probability of whether a current frame of the sample rate converted frames includes speech based on one or more of the cepstral coefficients, wherein the means for estimating is implemented as hardware within the VAD processor; and   means for applying a threshold to the estimated probability to convert the estimated probability to a binary feature, wherein the binary feature is an indication of voice activity, wherein the means for applying is implemented as hardware within the VAD processor.   
     
     
         89 . The VAD processor of  claim 88 , wherein the processed frames of the speech signal are generated via:
 means for generating a plurality of frames of the speech signal;   means for windowing each one of the plurality of frames;   means for computing a magnitude spectrum for each windowed frame;   means for computing a power spectrum of the magnitude spectrum;   means for filtering the power spectrum; and   means for generating non-linear transformation of the filtered power spectrum, wherein the generated non-linear transformation is the processed frames of the speech signal.   
     
     
         90 . The VAD processor of  claim 88 , wherein the means for converting the sample rate downsamples the processed frames by a factor of two. 
     
     
         91 . The VAD processor of  claim 88 , further comprising:
 means for smoothing the binary feature.   
     
     
         92 . The VAD processor of  claim 91 , wherein the means for smoothing adds seven frames before and after current frame that is estimated to include speech. 
     
     
         93 . The VAD processor of  claim 91 , wherein the means for smoothing comprises means for setting a bit according to whether current frame is estimated to include speech. 
     
     
         94 . The VAD processor of  claim 88 , wherein the means for estimating receives the current frame, two adjacent frames, and two cepstral coefficients calculated by the means for calculating. 
     
     
         95 . The DVR system of  claim 1 , wherein the transmitter transmits the indication of the detected voice activity from the VAD module before the at least one speech feature extracted by the FE module. 
     
     
         96 . The subscriber unit of  claim 15 , wherein the transmitter transmits the indication of the detected voice activity from the VAD module before the at least one speech feature extracted by the FE module. 
     
     
         97 . The method of  claim 37 , wherein transmitting the indication of the detected voice activity and the at least one feature comprises transmitting the indication of the detected voice activity before transmitting the at least one feature. 
     
     
         98 . The computer-readable storage medium of  claim 51 , wherein the instructions that cause the one or more processors to transmit the indication of the detected voice activity and the at least one feature comprise instructions that cause the one or more processors to transmit the indication of the detected voice activity before transmission of the at least one feature. 
     
     
         99 . The DVR system of  claim 65 , wherein the means for transmitting the indication of detected voice activity and the at least one speech feature comprises means for transmitting the indication of detected voice activity before transmitting the at least one speech feature. 
     
     
         100 . The subscriber unit of  claim 77 , wherein the means for transmitting the indication of detected voice activity and the at least one speech feature comprises means for transmitting the indication of detected voice activity before transmitting the at least one speech feature.